Checking an exterior appearance of an industrial product involves capturing an image of the exterior appearance of the industrial product with an image capturing device and determining whether or not there is a defect thereon. Here, in the field of factory automation (FA), noise generated in an image capturing environment, as well as fluctuation of shadow and brightness, often affects the captured image. Thus, it is desirable that an image processing algorithm extract a defect portion robustly against environmental change. Also, when an appearance checking device is operated to check an exterior appearance of an industrial product, the image processing algorithm is sometimes to be reconstructed because of change in checking environment, such as change of check target and improvement of the appearance checking device. Hence, the appearance checking device needs to be capable of reconstructing its image processing method easily.
An image processing apparatus in the appearance checking device creates an output image by processing an image captured by a camera or the like with an image processing filter. Here, it is known that the conventional image processing apparatus produces an image processing filter by performing evolutionary computation based on genetic programming. The genetic programming models a procedure of biological evolution to execute a crossover process and a mutation process on a tree-structured image processing filter, for the purpose of generating a plurality of new image processing filters. An initial image processing filter is replaced by a new image processing filter of high fitness, and the aforementioned procedure is repeated to change generations of image processing filters, until an optimal image processing filter is found.
Such conventional image processing filter producing process includes an image processing filter selecting process in which an image region that is specified by a worker is processed. A plurality of image processing filters are used to process an image. Then, a plurality of output images processed by the image processing filters are displayed on a display, so that the worker can select a preferable output image from among them. Then, the image processing filter that has produced the image selected by the worker is subjected to an evolution process.
See, for example, Japanese Laid-open Patent Publication No. 2010-26945.
In the conventional image processing filter producing process, a worker confirms output images after filtering process to select an image processing filter. That is, the fitness of an image processing filter is judged by comparing images or their pixels before and after the filtering process of the image processing filter. However, when images or their pixels are compared between before and after the filtering process, variation of edge line widths after the filtering process and an error in teaching may decrease the fitness of the image processing filter even if the image processing filter leads to an effective output result. Hence, in a conventional fitness calculating method, an effective process may be dropped out of selection through optimization by genetic programming, in some cases.